ALIF EXPLORATION
Selected Variables
base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Levels Previously operated - Lower
- LGap
- RLL
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic TiltAge
Proportion of na: 0% 
Gender
Proportion of na: 0%| Female | Male | |
|---|---|---|
| No | 342 | 91 |
| Yes | 47 | 16 |
Prior Spine Surgery
Proportion of na: 0.2%| NA | No | Yes | |
|---|---|---|---|
| No | 1 | 274 | 158 |
| Yes | 0 | 31 | 32 |
1st surgeon: experience in ASD surgery
Proportion of na: 0%| 2-10 years | More than 10 years | |
|---|---|---|
| No | 109 | 324 |
| Yes | 5 | 58 |
ASA classification
Proportion of na: 0.6% 
Decompression
Proportion of na: 0%| No | Yes | |
|---|---|---|
| No | 240 | 193 |
| Yes | 38 | 25 |
Osteotomy
Proportion of na: 0%| No | Yes | |
|---|---|---|
| No | 209 | 224 |
| Yes | 20 | 43 |
3CO
Proportion of na: 0%| No | Yes | |
|---|---|---|
| No | 382 | 51 |
| Yes | 47 | 16 |
SPOs
Proportion of na: 0% 
BMI_First Visit
Proportion of na: 1.8% 
Tobacco use_First Visit
Proportion of na: 2.6%| Current | Ex-User | NA | Non-User | |
|---|---|---|---|---|
| No | 74 | 84 | 11 | 264 |
| Yes | 11 | 14 | 2 | 36 |
Osteoporosis / osteopenia
Proportion of na: 0%| No | Yes | |
|---|---|---|
| No | 340 | 93 |
| Yes | 54 | 9 |
Levels Previously operated - Lower
Proportion of na: 63.1%| C | Iliac | L | NA | S | T | |
|---|---|---|---|---|---|---|
| No | 6 | 8 | 91 | 279 | 43 | 6 |
| Yes | 0 | 4 | 16 | 34 | 8 | 1 |
LGap
Proportion of na: 2.6% 
RLL
Proportion of na: 2.6% 
Number of Interbody Fusions
Proportion of na: 0% 
Posterior Instrumented Fusion: Upper / Lower Levels
Proportion of na: 0%| Iliac+S | L | T | |
|---|---|---|---|
| No | 301 | 128 | 4 |
| Yes | 52 | 11 | 0 |
LL-Lordosis Difference
Proportion of na: 2.6% 
Propensity Scores Common Support
## Loading required package: lattice##
## Attaching package: 'lattice'## The following object is masked from 'package:boot':
##
## melanomaModel Stats
- Treatment proportion: 0.127
- Model Type: elastic_net
- Accuracy: 0.8982979
- Params: alpha: 0.1 lambda: 0.0068096
Model Coefficients
Bootstraping replicas: 50
Average Treatment Effects - Quality Life
Outcome: 2Y. ODI - Score (%)
Distribution:
0% 25% 50% 75% 100%
-67 -27 -14 -4 40
Model Type: elastic_net
RMSE: 17.5959997182535
Params: alpha: 0.6142857
lambda: 0.4865523
ATE (Yes-No): 0
Observational differences in treatment 0.206 (Yes-No)
Alif 2Y. ODI - Score (%)
1: Yes -14.61290
2: No -14.81887`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 2Y. SRS22 - SRS Subtotal score
Distribution:
0% 25% 50% 75% 100%
-0.950 0.215 0.700 1.160 3.050
Model Type: elastic_net
RMSE: 0.679982046849019
Params: alpha: 0.1
lambda: 0.0018896
ATE (Yes-No): 0.0426602703645799
Observational differences in treatment 0.18 (Yes-No)
Alif 2Y. SRS22 - SRS Subtotal score
1: Yes 0.8662500
2: No 0.6858672`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 2Y. SF36 - MCS
Distribution:
0% 25% 50% 75% 100%
-33.82 -3.69 3.72 12.94 39.74
Model Type: elastic_net
RMSE: 12.8461823109938
Params: alpha: 0.55
lambda: 0.383285
ATE (Yes-No): 0
Observational differences in treatment -0.242 (Yes-No)
Alif 2Y. SF36 - MCS
1: Yes 3.941111
2: No 4.183228`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 2Y. SF36 - PCS
Distribution:
0% 25% 50% 75% 100%
-18.94 0.72 6.64 12.42 38.99
Model Type: elastic_net
RMSE: 9.18209807459122
Params: alpha: 0.6785714
lambda: 0.4118198
ATE (Yes-No): 0
Observational differences in treatment 1.02 (Yes-No)
Alif 2Y. SF36 - PCS
1: Yes 7.687037
2: No 6.666929`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Average Treatment Effects - Radiology
Outcome: 6W. Major curve Cobb angle
Distribution:
0% 25% 50% 75% 100%
-72.000 -20.510 -10.000 -3.905 30.800
Model Type: elastic_net
RMSE: 14.0102112981178
Params: alpha: 0.55
lambda: 0.8562396
ATE (Yes-No): 0
Observational differences in treatment -1.878 (Yes-No)
Alif 6W. Major curve Cobb angle
1: Yes -14.77412
2: No -12.89612`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 1Y. Major curve Cobb angle
Distribution:
0% 25% 50% 75% 100%
-64.00 -22.69 -10.36 -3.00 22.44
Model Type: boosting
RMSE: 14.4469288710997
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75
ATE (Yes-No): -1.66465923537967
Observational differences in treatment -2.136 (Yes-No)
Alif 1Y. Major curve Cobb angle
1: Yes -15.53875
2: No -13.40321`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 6W. T1 Sagittal Tilt
Distribution:
0% 25% 50% 75% 100%
-23.631420 -6.000000 -1.411482 1.689195 18.000000
Model Type: boosting
RMSE: 5.94387830781916
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.7142857
ATE (Yes-No): -2.24813080830117
Observational differences in treatment -2.954 (Yes-No)
Alif 6W. T1 Sagittal Tilt
1: Yes -4.965070
2: No -2.010682`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 1Y. T1 Sagittal Tilt
Distribution:
0% 25% 50% 75% 100%
-30.098675 -5.808565 -2.187195 1.000000 20.000000
Model Type: elastic_net
RMSE: 5.83983563570657
Params: alpha: 0.1
lambda: 0.0364041
ATE (Yes-No): -1.29985806546258
Observational differences in treatment -2.397 (Yes-No)
Alif 1Y. T1 Sagittal Tilt
1: Yes -4.840364
2: No -2.442963`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 6W. Sagittal Balance
Distribution:
0% 25% 50% 75% 100%
-194.79 -69.00 -26.50 3.96 114.15
Model Type: boosting
RMSE: 53.413447232778
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5357143
ATE (Yes-No): -15.8937886710836
Observational differences in treatment -33.252 (Yes-No)
Alif 6W. Sagittal Balance
1: Yes -63.58620
2: No -30.33438`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 1Y. Sagittal Balance
Distribution:
0% 25% 50% 75% 100%
-237.47 -67.07 -30.52 5.84 109.54
Model Type: boosting
RMSE: 52.9144031771406
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0
ATE (Yes-No): -20.3390487647309
Observational differences in treatment -30.124 (Yes-No)
Alif 1Y. Sagittal Balance
1: Yes -59.74368
2: No -29.61955`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 6W. Global Tilt
Distribution:
0% 25% 50% 75% 100%
-68.62 -17.58 -6.00 1.52 149.41
Model Type: boosting
RMSE: 14.2043051559946
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.6428571
ATE (Yes-No): -4.8742459429936
Observational differences in treatment -10.536 (Yes-No)
Alif 6W. Global Tilt
1: Yes -17.585294
2: No -7.049362`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 1Y. Global Tilt
Distribution:
0% 25% 50% 75% 100%
-62.630 -16.000 -6.465 1.000 26.000
Model Type: elastic_net
RMSE: 12.0502873635078
Params: alpha: 0.1
lambda: 0.2264674
ATE (Yes-No): -7.3543129635547
Observational differences in treatment -10.582 (Yes-No)
Alif 1Y. Global Tilt
1: Yes -16.991026
2: No -6.409266`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
0% 25% 50% 75% 100%
-94.930 -24.045 -9.355 0.140 29.000
Model Type: boosting
RMSE: 15.7600356488601
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.8571429
ATE (Yes-No): -2.23254487160861
Observational differences in treatment -10.943 (Yes-No)
Alif 6W. Lordosis (top of L1-S1)
1: Yes -21.82942
2: No -10.88661`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
0% 25% 50% 75% 100%
-94.63 -25.71 -9.00 0.00 23.38
Model Type: boosting
RMSE: 16.3496121974448
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5714286
ATE (Yes-No): -6.09500955049753
Observational differences in treatment -13.732 (Yes-No)
Alif 1Y. Lordosis (top of L1-S1)
1: Yes -24.84000
2: No -11.10803`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 6W. LGap
Distribution:
0% 25% 50% 75% 100%
-96.12340 -24.28110 -9.06300 0.31715 78.92000
Model Type: boosting
RMSE: 17.3311857535895
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.6428571
ATE (Yes-No): -1.61978158240683
Observational differences in treatment -11.262 (Yes-No)
Alif 6W. LGap
1: Yes -21.70294
2: No -10.44091`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 1Y. LGap
Distribution:
0% 25% 50% 75% 100%
-94.8082 -25.2564 -9.0618 0.1456 22.0800
Model Type: boosting
RMSE: 16.2272726010664
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.6071429
ATE (Yes-No): -4.84086064302004
Observational differences in treatment -13.59 (Yes-No)
Alif 1Y. LGap
1: Yes -24.56838
2: No -10.97887`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 6W. Pelvic Tilt
Distribution:
0% 25% 50% 75% 100%
-36.41 -8.33 -2.42 2.00 14.42
Model Type: boosting
RMSE: 7.71581556709911
Params: nrounds: 50.0
max_depth: 2
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5714286
ATE (Yes-No): -1.99650931844323
Observational differences in treatment -6.345 (Yes-No)
Alif 6W. Pelvic Tilt
1: Yes -9.369216
2: No -3.024645`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Outcome: 1Y. Pelvic Tilt
Distribution:
0% 25% 50% 75% 100%
-26.62 -7.10 -2.14 2.00 23.00
Model Type: boosting
RMSE: 7.0835730052974
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.7142857
ATE (Yes-No): -5.31459360141021
Observational differences in treatment -5.775 (Yes-No)
Alif 1Y. Pelvic Tilt
1: Yes -8.026750
2: No -2.251544`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Average Treatment Effects - Complications
Outcome: complication
Distribution:
Proportion
0.296837
Model Type: boosting
Accuracy: 0.696017675036188
Params: nrounds: 50.0
max_depth: 12
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75
ATE (Yes-No): 0
Observational differences in treatment 0.006 (Yes-No)
Alif complication
1: Yes 0.3023256
2: No 0.2961957`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Average Treatment Effects - Nombre Revisions
Outcome: reinterventions
Distribution:
0% 25% 50% 75% 100%
0 0 0 1 6
Model Type: boosting
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.6785714
ATE (Yes-No): 0
Observational differences in treatment 0.042 (Yes-No)
Alif reinterventions
1: No 0.4701087
2: Yes 0.5116279`geom_smooth()` using method = 'loess' and formula 'y ~ x'
